What does Dynochem do?

Dynochem software offers dynamic chemical process simulation and optimization by combining data with equipment characteristics and powerful predictive models. The calculation of optimal process conditions and equipment utilization makes it possible to deliver better processes using fewer overall experiments.  

An extensive library of template models is available to all users. The application of models is supported by free training, expert guidance, and project support. Some of the most commonly used Dynochem applications include:

  • Solvent temperature-dependent properties and solvent interaction predictions
  • Mixing and Heat Transfer assessment and characterization tools for STRs and PFRs
  • Simulation of heating or cooling a reactor to quickly calculate the time required to bring a reactor to the recipe temperature for a reaction, crystallization, or other operation
  • Reaction models for homogeneous and heterogeneous reactions in batch and flow chemistry operations
  • Crystallization models to predict particle size distribution (PSD) 
  • Dynamic models of batch solvent swap operations in continuous and put-and-take modes to predict the amount of fresh solvent required on scale-up and the operation time
  • Tools to characterize key filtration process parameters and predict scale-up performance from lab to plant filtration equipment and between filtration and centrifugation operations

How can this help in process development?

With Dynochem, teams of chemists and chemical engineers in process development and primary manufacturing can scale up, troubleshoot, and optimize reaction, workup, and isolation stages with confidence. Common projects where Dynochem is used include:

  • Ensure adequate mixing and mixing equivalence on scale-up
  • Predict thermal process safety and effect of addition rates and heat transfer on Time to Maximum Rate (TMR) and Maximum Temperature of Synthesis Reaction (MTSR) 
  • Robust crystallization design from standard solubility experiments to optimize seeding strategy, cooling profile, and anti-solvent addition rate to promote crystal growth
  • De-bottleneck and optimize filtration and centrifugation equipment selection and operation
  • Design continuous manufacturing equipment and operating conditions to fit process chemistry needs

What is the difference between Dynochem and iC Safety?

The key difference is that Dynochem predictions are based on a kinetic model, which allows great flexibility in determining the behavior of your process in different equipment setups, and thus allows the optimization of a safe process in silico. In comparison, iC Safety is based on determining standard metrics like MAT and DTad from RC1 data and using this to determine the Stoessel safety class. It won't directly allow you to predict, for example, what conditions would allow you to run the process safely when transferring to a large-scale vessel. The two approaches are complementary, however, and both are very robust!

What template models does Dynochem include?

Dynochem template models include:

  • Mixing and Heat Transfer in stirred tank reactors
  • Reactions in batch and semi-batch reactors
  • Binary and Ternary phase equilibria
  • Batch distillation and solvent swap
  • Crystallization
  • Filtration & centrifugation
  • Drying

Common operations in continuous processing such as:

  • Mixing and Heat Transfer in PFRs
  • Reactions in CSTRs and PFRs
  • Crystallization in CSTRs
  • Counter-current extraction
  • Wiped-film evaporator

Scale-up of Batch Crystallization From Lab to Plant

Batch Crystallizer Scale-Up and Design

Improve Process Development, Optimization, and Scale-up With Real-Time Crystal Population Measurement

Sikker, uovervåget dosering i kemisk udvikling og opskalering

Sikker, uovervåget dosering i kemisk udvikling og opskalering

Automatiser dosering og udfør meget eksoterme reaktioner uden opsyn

Lubrizol - procesudvikling og opskalering

Lubrizols effektive opskalerings- og proceskontrol

At bygge bro mellem laboratorie- og In Situ-procesanalyse

Effektive Design of Experiment-undersøgelser

Effektive Design of Experiment-undersøgelser

DoE til innovativ kemisk udvikling i laboratoriet